Logistics Startup of the Month: We4Sea

Every month we select one Logistics Startup which represents a positive example of innovation in logistics and has the potential to alter the way the industry operates. This month, Transmetrics selected We4Sea, a fuel-efficiency solution for ships, as the March “Logistics Startup of the Month” for its outstanding usage of data analytics to save fuel, decrease emissions and accelerate the sustainability of the shipping industry. In order to learn more about the company and what they do, we talked with Dan Veen, CEO of We4Sea, and asked him several questions about the business.

6 Ways to Improve Logistics Performance With Big Data

From improving healthcare to suggesting what movies to watch, big data is being used to improve our lives on a daily basis. Accordingly, businesses across the globe — and across industries — have spent extensive amounts of their marketing budgets on using this data to gain expert insight into both their businesses and their customers.

Is data quality an obstacle for predictive analytics optimization?

Is data quality an obstacle for predictive analytics optimization?

First of all, If you try to put any data that you have into the predictive algorithm, it is going to predict some results, but they’re not going to be what you need. Instead, the algorithm is going to deliver you a set of very low-quality predictions. In other words, it follows the principle “garbage in, garbage out”, in which the decision-making might be flawed due to incomplete, or imprecise data. Improving the quality of the historical logistics data is extremely difficult but it is a must before you even start thinking about predictive optimization.